"""simple docstring"""


from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
	import tensorflow as tf

	from transformers import TFBlipTextModel
	from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=12      ,					_lowerCamelCase=7      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=99      ,					_lowerCamelCase=32      ,					_lowerCamelCase=32      ,					_lowerCamelCase=2      ,					_lowerCamelCase=4      ,					_lowerCamelCase=37      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=512      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=0      ,					_lowerCamelCase=None      ,					):
										lowerCamelCase__   =parent
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =seq_length
										lowerCamelCase__   =is_training
										lowerCamelCase__   =use_input_mask
										lowerCamelCase__   =use_labels
										lowerCamelCase__   =vocab_size
										lowerCamelCase__   =hidden_size
										lowerCamelCase__   =projection_dim
										lowerCamelCase__   =num_hidden_layers
										lowerCamelCase__   =num_attention_heads
										lowerCamelCase__   =intermediate_size
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =initializer_range
										lowerCamelCase__   =scope
										lowerCamelCase__   =bos_token_id

				def 						_a      ( self ):
										lowerCamelCase__   =ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

										lowerCamelCase__   =None
										if self.use_input_mask:
																lowerCamelCase__   =random_attention_mask([self.batch_size, self.seq_length] )

										if input_mask is not None:
																lowerCamelCase__   =input_mask.numpy()
																lowerCamelCase__   , lowerCamelCase__   =input_mask.shape
																lowerCamelCase__   =np.random.randint(1      ,					seq_length - 1      ,					size=(batch_size,) )
																for batch_idx, start_index in enumerate(_lowerCamelCase ):
																						lowerCamelCase__   =1
																						lowerCamelCase__   =0

										lowerCamelCase__   =self.get_config()

										return config, input_ids, tf.convert_to_tensor(_lowerCamelCase )

				def 						_a      ( self ):
										return BlipTextConfig(
										    vocab_size=self.vocab_size      ,					hidden_size=self.hidden_size      ,					projection_dim=self.projection_dim      ,					num_hidden_layers=self.num_hidden_layers      ,					num_attention_heads=self.num_attention_heads      ,					intermediate_size=self.intermediate_size      ,					dropout=self.dropout      ,					attention_dropout=self.attention_dropout      ,					max_position_embeddings=self.max_position_embeddings      ,					initializer_range=self.initializer_range      ,					bos_token_id=self.bos_token_id      ,					)

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =TFBlipTextModel(config=_lowerCamelCase )
										lowerCamelCase__   =model(_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					training=_lowerCamelCase )
										lowerCamelCase__   =model(_lowerCamelCase      ,					training=_lowerCamelCase )
										self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size) )
										self.parent.assertEqual(result.pooler_output.shape      ,					(self.batch_size, self.hidden_size) )






				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =config_and_inputs
										lowerCamelCase__   ={"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_tf
class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):
				A__ :  Union[str, Any]    				=			(TFBlipTextModel,) if is_tf_available() else ()
				A__ :  List[Any]    				=			False
				A__ :  Tuple    				=			False
				A__ :  str    				=			False

				def 						_a      ( self ):
										lowerCamelCase__   =BlipTextModelTester(self )
										lowerCamelCase__   =ConfigTester(self      ,					config_class=_lowerCamelCase      ,					hidden_size=37 )

				def 						_a      ( self ):
										self.config_tester.run_common_tests()

				def 						_a      ( self ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase )

				def 						_a      ( self ):
										pass

				def 						_a      ( self ):
										pass

				@unittest.skip(reason="Blip does not use inputs_embeds" )
				def 						_a      ( self ):
										pass

				@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
				def 						_a      ( self ):
										pass

				@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
				def 						_a      ( self ):
										pass

				@slow
				def 						_a      ( self ):
										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowerCamelCase__   =TFBlipTextModel.from_pretrained(_lowerCamelCase )
																self.assertIsNotNone(_lowerCamelCase )






				def 						_a      ( self      ,					_lowerCamelCase=True ):
										super().test_pt_tf_model_equivalence(allow_missing_keys=_lowerCamelCase )



"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   list[float]:




						'''simple docstring'''



						if radian_mode:
												return [magnitude * cos(__lowerCAmelCase							), magnitude * sin(__lowerCAmelCase							)]
						return [magnitude * cos(radians(__lowerCAmelCase							)							), magnitude * sin(radians(__lowerCAmelCase							)							)]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 10**-1							)				->   bool:




						'''simple docstring'''



						lowerCamelCase__   =cross(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =sum(__lowerCAmelCase							)
						return abs(__lowerCAmelCase							) < eps


if __name__ == "__main__":
	# Test to check if it works
	a				=array(
	    [
	        polar_force(718.4, 180 - 30),
	        polar_force(879.54, 45),
	        polar_force(100, -90),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem 1 in image_data/2D_problems.jpg
	a				=array(
	    [
	        polar_force(30 * 9.81, 15),
	        polar_force(215, 180 - 45),
	        polar_force(264, 90 - 30),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem in image_data/2D_problems_1.jpg
	a				=array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

	a				=array([[0, 0], [6, 0], [10, 0], [12, 0]])

	assert in_static_equilibrium(forces, location)

	import doctest

	doctest.testmod()


